Tracking Accuracy During Passive Tendon Movement

finds the wrong target block (irregular movement), these errors were evaluated independently and, the results obtained for both the stationary and irregular errors during passive and active movements are given in Sections 4.4 and 4.5 respectively. All the results are discussed in Section 4.6 and the best performing algorithm identified.

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4.2 Tracking Accuracy During Passive Tendon Movement

This experiment evaluates the performance of the four tracking algorithms when the tendon was relaxed at 0% maximum voluntary contraction (MVC), and the probe was moved over the sagittal plane of the patella tendon (knee) along the skin surface for a distance of approximately 2-3 cm. The areas of interest for this study were the tendentious areas of the patella (knee) and gastrocnemius (ankle). Two experiments took place as explained in Chapter 3.

The errors are calculated from the displacements of the ROI. In the first experiment, which involved the patella tendon, the mean displacement of the ROI from automatic tracking and manual measurement from ImageJ were shown in table 4.1.

Test

Mean Displacement (mm)

MSE NCC NCCMSE LK Manual Knee Passive 14.50±1.38 15.79±1.57 14.90±1.30 13.48±1.02 15.35±1.22 Difference (mm) 0.85±1.84 -0.44±1.99 0.46±1.79 1.87±1.59 -

Table 4.1: Mean displacement of 10 samples and difference (in millimetres) of each tracking algorithm against the standard manual measurement of passive movement at the patella tendon.

The manual measurement is based on standard measurement method and each algorithm mean value is compared against the value from the manual measurement to give the difference between mean as shown in equation 4.1:

d=man−algo (4.1)

where d is the difference between two means. The standard error of the difference between means is shown in equation 4.2:

σman-algo = σman 2 +σ

algo

2 _(4.2)

where σ is the variance of the sample, n is the sample size, algo is the measurement using the algorithm and man is the manual measurement.

The NCC tracking algorithm gave very little difference against manual measurement with the difference value of -0.44±1.99mm while NCCMSE gave the difference of 0.46±1.79mm. The MSE tracking algorithm and manual measurement gave a difference of 0.85±1.84mm while the Lucas-Kanade tracking algorithm gave the highest difference of 1.87±1.59mm. However, multiple t-tests revealed no significant differences (p>0.05) were found between the algorithms and the manual measurement. In this experiment, the NCC and NCCMSE tracking algorithms gave the closest values to the manual measurement compared to the MSE and LK.

In the second experiment, the passive movement of the myotendinous junction of the gastrocnemius tendon was tracked. The same apparatus and procedures (as previous experiments) were used in this study. An addition of EMG was employed to calculate co-contraction torque. The mean total displacements are plotted against manual measurement as shown in table 4.2.

Test

Mean Displacement (mm)

MSE NCC NCCMSE LK Manual Gastrocnemius

Passive 18.76±1.56 20.26±0.80 20.68±0.93 22.42±0.94 20.90±1.31 Difference (mm) 2.14±2.04 0.63±1.54 0.22±1.61 -1.53±1.62 -

Table 4.2: Mean displacement of 10 samples and difference (in millimetres) of each tracking algorithm against the standard manual measurement of passive movement at the medial gastrocnemius tendon.

Again, the manual measurement is based on standard measurement method and all algorithms are compared against the value from manual measurement. The same multiple t-tests were used for statistical analysis, and the results showed that the NCC and NCCMSE tracking algorithms were significantly different to both Lucas- Kanade and MSE tracking algorithm (p<0.05). However, none of the algorithms was found to be significantly different (p>0.05) to the manual measurement. The NCCMSE mean displacement value had the smallest difference of 0.22±1.61mm, followed by NCC at 0.63±1.54mm. Both NCC and NCCMSE tracking algorithms gave < 1mm difference compared to LK with the value of -1.53±1.62mm and MSE tracking algorithm gave the worst difference with the value of 2.14±2.04mm.

The results showed that the LK tracking algorithm struggled to track the speckle images, particularly when tracked at the areas of the patella tendon, which had no significant identifiable feature such as edges or shapes. The results improved when tracked over the media gastrocnemius tendon due to the ‘Y’ shape existing between the intersection of muscle and the tendon. However, the MSE tracking algorithm proved to be poor in both experiments because it was less sensitive to the heavily formed speckle. Both the NCC and NCCMSE tracking algorithms however, gave little differences in terms of tracking at both patella and gastrocnemius tendon, but the NCCMSE tracking algorithm was shown to give the better tracking result.

The computational costs (measured in seconds) for both experiments were also recorded (see Figure 4.1). The figure showed that among all algorithms, the LK tracking algorithm was found to give the highest computational cost with overall

performance of 470±30 seconds to complete the tracking process. The NCCMSE tracking algorithm performed better than the LK tracking algorithm, but because it consisted of an SNR decision selector, it required more computation operations, and showed a total mean time of 39±1 seconds. The NCC tracking algorithm was shown to have the least mean computation cost of 13±1 seconds while the MSE tracking algorithm gave a mean of 27±1 seconds. The passive movement experiments showed the NCCMSE tracking algorithm gave the best results but with the cost of computational time. The NCC tracking algorithm however, has only <5% different than the NCCMSE tracking algorithm with less computational cost. The LK tracking algorithms were shown to be unsuitable in this experiment.

Figure 4.1: Total mean computational cost for both experiments (in seconds) between the algorithms.